Role Overview
Scale AI is hiring a Staff Machine Learning Engineer to lead the design and deployment of agentic AI systems for critical government environments in Denver, Colorado or Washington, DC. This role suits an experienced engineer who wants to shape technical direction and build foundational infrastructure that allows artificial intelligence to reason, plan, and act reliably on a national scale.
Key Responsibilities
Lead the architecture and implementation of agentic AI systems, focusing on long-horizon reasoning, orchestration, and system-level reliability.
Build and scale agents capable of performing complex geospatial reasoning, including interpreting and reasoning over maps and spatial data.
Design and improve retrieval systems across large collections of static and semi-structured documents to surface high-signal context efficiently.
Fine-tune and evaluate embedding models to improve recall and precision for mission-critical datasets.
Create memory systems enabling agents to persist state, operate over long contexts, and learn from previous interactions.
Own and evolve shared agentic infrastructure and core libraries for reuse across teams, products, and Public Sector contracts.
Define evaluation strategies for agentic systems, such as robustness testing, failure-mode analysis, and production regression testing.
Partner closely with engineering managers, product leaders, and researchers to scope high-impact initiatives.
Serve as a technical mentor, elevating system design, machine learning rigor, and production readiness across the organization.
Engage in light travel, approximately 10 percent of the time, for customer interactions and team requirements.
Requirements
Active TS security clearance.
Minimum of 8 years of experience building and deploying applied machine learning systems in production environments.
Deep expertise with agentic systems, autonomous workflows, or machine learning systems that reason and act across multiple steps.
Strong background in machine learning systems engineering, encompassing model serving, pipelines, monitoring, and evaluation.
Hands-on experience working with retrieval systems, embeddings, or representation learning.
Proficiency in Python and modern machine learning frameworks like PyTorch, with end-to-end system design capabilities.
Demonstrated capability to operate at a Staff level, setting technical direction and driving zero-to-one initiatives to production.
Nice to Have
Experience deploying machine learning systems into air-gapped, classified, or disconnected environments.
Prior work with the Department of Defense, the intelligence community, or federal mission users.
Hands-on experience with geospatial data or GEOINT, including reasoning over maps, imagery, or spatial reference systems.
Depth in model adaptation, such as training or fine-tuning embedding models, instruction tuning, LoRA, PEFT, or RLHF.
Experience building evaluation infrastructure for non-deterministic systems, including LLM-as-judge or regression suites for agent behavior.
Track record of turning forward-deployed prototypes into supported, documented capabilities.
Skills and Tools
Python
PyTorch
LLM
Geospatial reasoning and GEOINT
Retrieval systems and embeddings
Agentic infrastructure and model serving
Salary and Benefits
Comprehensive health, dental, and vision coverage.
Retirement benefits and generous paid time off.
Learning and development stipend, plus potential commuter stipend eligibility.
Equity-based compensation subject to Board of Director approval.
Washington, DC base salary range: $274,400 to $343,000 USD (ranges for other locations determined during the interview process).
About the Company
Scale AI develops reliable artificial intelligence systems for major decisions, providing high-quality data and full-stack technologies that power leading models. The organization works with industry leaders and U.S. government agencies, including the Army and Air Force, to help enterprises and governments build, deploy, and oversee impactful AI applications. Scale AI operates as an inclusive, equal opportunity workplace.
How to Apply
Interested candidates should apply directly on the official company careers page.
